---
title: Tensorlake
---

Tensorlake is the AI Data Cloud that reliably transforms data from unstructured sources into ingestion-ready formats for AI Applications.

The `langchain-tensorlake` package provides seamless integration between [Tensorlake](https://tensorlake.ai) and [LangChain](https://langchain.com),
enabling you to build sophisticated document processing agents with enhanced parsing features, like signature detection.

## Tensorlake feature overview

Tensorlake gives you tools to:
- Extract: Schema-driven structured data extraction to pull out specific fields from documents.
- Parse: Convert documents to markdown to build RAG/Knowledge Graph systems.
- Orchestrate: Build programmable workflows for large-scale ingestion and enrichment of Documents, Text, Audio, Video and more.

Learn more at [docs.tensorlake.ai](https://docs.tensorlake.ai/introduction)

---

## Installation

<CodeGroup>
```bash pip
pip install -U langchain-tensorlake
```

```bash uv
uv add langchain-tensorlake
```
</CodeGroup>

---

## Examples

Follow a [full tutorial](https://docs.tensorlake.ai/examples/tutorials/real-estate-agent-with-langgraph-cli) on how to detect signatures in unstructured documents using the `langchain-tensorlake` tool.

Or check out this [colab notebook](https://colab.research.google.com/drive/1VRWIPCWYnjcRtQL864Bqm9CJ6g4EpRqs?usp=sharing) for a quick start.

---
## Quick Start

### 1. Set up your environment

You should configure credentials for Tensorlake and OpenAI by setting the following environment variables:
```
export TENSORLAKE_API_KEY="your-tensorlake-api-key"
export OPENAI_API_KEY = "your-openai-api-key"
```

Get your Tensorlake API key from the [Tensorlake Cloud Console](https://cloud.tensorlake.ai/). New users get 100 free credits.

### 2. Import necessary packages

```python
from langchain_tensorlake import document_markdown_tool
from langchain.agents import create_agent
import asyncio
import os
```

### 3. Build a Signature Detection Agent

```python
async def main(question):
    # Create the agent with the Tensorlake tool
    agent = create_agent(
            model="openai:gpt-4o-mini",
            tools=[document_markdown_tool],
            prompt=(
                """
                I have a document that needs to be parsed. \n\nPlease parse this document and answer the question about it.
                """
            ),
            name="real-estate-agent",
        )

    # Run the agent
    result = await agent.ainvoke({"messages": [{"role": "user", "content": question}]})

    # Print the result
    print(result["messages"][-1].content)
```

*Note:* We highly recommend using `openai` as the agent model to ensure the agent sets the right parsing parameters

### 4. Example Usage

```python
# Define the path to the document to be parsed
path = "path/to/your/document.pdf"

# Define the question to be asked and create the agent
question = f"What contextual information can you extract about the signatures in my document found at {path}?"

if __name__ == "__main__":
    asyncio.run(main(question))
```

## Need help?

Reach out to us on [Slack](https://join.slack.com/t/tensorlakecloud/shared_invite/zt-32fq4nmib-gO0OM5RIar3zLOBm~ZGqKg) or on the
[package repository on GitHub](https://github.com/tensorlakeai/langchain-tensorlake) directly.
